Chronic heart failure (HF) occurs when a heart is unable to consistently pump blood at an adequate rate in response to the filling pressure. To improve the ability of the heart to pump blood, congestive heart failure patients, classified as having New York Heart Association (NYHA) class status of II to IV HF, may require implantable medical devices (IMDs) such as implantable cardioverter defibrillators (ICDs) and cardiac resynchronization devices with defibrillation capability (CRT-Ds). Despite using IMDs to improve heart function, some HF patients may require hospitalization. Global health care systems incur billions of dollars each year due to heart failure hospitalizations (HFHs). Identifying patients at risk of HFH to enable timely intervention and prevent expensive hospitalization remains a challenge. Implantable cardioverter defibrillators (ICDs) and cardiac resynchronization devices with defibrillation capability (CRT-Ds) are configured to acquire data for a variety of diagnostic metrics that change with HF status and collectively have the potential to signal an increasing risk of HFH. Diagnostic parameter data collected by IMDs include activity, day and night heart rate (NHR), atrial tachycardia/atrial fibrillation (AT/AF) burden, mean rate during AT/AF, percent CRT pacing, number of shocks, and intrathoracic impedance. Additionally, preset or programmable thresholds for diagnostic metrics, when crossed, trigger a notification, referred to as device observation. Each device observation is recorded in an IMD report.
One conventional method for predicting HFH risk is US pregrant publication No. 2012/0253207 A1, entitled Heart Failure Monitoring, to Sarkar et al. Sarkar et al. is directed to a post-discharge period in which the IMD is interrogated remotely through wireless transmission to evaluate the prognosis of the patient using device diagnostics. For example, an evaluation can be performed during a 7 day period post discharge such that a determination is made as whether the patient had 1-6 days of AF burden>6 hrs, poor rate control (i.e. 1 day of AF>6 hrs and rate>90 bpm), a fluid index greater than 60 or 100 ohm-days, night heart rate>85 bpm, heart rate variability less than or equal to 40 ms, ventricular tachycardia, or % CRT pacing<90%. If any two of the listed parameters were met, the patient is considered high risk for a re-admission and is designated for post discharge care (e.g. nurse call or treatment modifications). If no criterion is met, the patient is considered at lower risk for HFH and less attention is provided to that patient. While Sarkar et al. provides useful information as to calculating the risk of HFH, it is desirable to provide gradations of HFH risk. Additionally, it is also desirable to provide develop a method that simplifies the HFH risk calculation without regard as to whether two different listed parameters were triggered.
Another method for estimating HFH risk is disclosed in a risk stratification study by Martin R. Cowie et al., Development And Validation Of An Integrated Diagnostic Algorithm Derived From Parameters Monitored In Implantable Devices For Identifying Patients At Risk For Heart Failure Hospitalization In An Ambulatory Setting Which Disclosed That Various IMD Diagnostics Variables Could Be Combined For The Previous 30-Days Using A Heuristic Approach To Assess Patient HF Risk In The Next 30 Days, European Heart Journal (Aug. 14, 2013) (hereinafter referred to as the EHJ article).
Yet another method involves U.S. Pat. No. 8,768,718 B2 to Cazares et al. Cazares et al. uses between-patient comparisons for risk stratification of future heart failure decompensation. Current patient data is collected by a patient monitoring device. A reference group related to the patient is determined. A reference group dataset is selected from the reference group. The dataset includes patient data that is of a similar type received from the patient monitoring device. A model of the reference group dataset is generated using a probability distribution function and automatically compared to the received physiological data to a model to derive an index for the patient. This method is cumbersome. For example, the method requires a model of the reference group dataset is generated and automatically compared using a probability distribution function. Numerous other methods include various complexities such as U.S. Pat. No. 8,777,850 to Cho et al., US Pregrant Application 2012/0109243 to Hettrick et al. U.S. Pat. No. 7,682,316 B2 to Anderson et al.
While a number of methods can be used to predict HFH risk, improvements can be made. For example, it is desirable to develop a method to estimate risk of HFH that can be easily implemented without unduly burdening healthcare providers. Additionally, it would be desirable to have a method or system that was able to present increased gradations of HFH risk instead of broad risk categories such as high risk and low risk.